Systems and methods for reconstructing a trajectory from anonymized data

ABSTRACT

Systems and methods for reconstructing a trajectory from anonymized data are provided. In some aspects, a method includes receiving anonymized data corresponding to a trajectory of a user or object, and assembling, based on the anonymized data, a state-space model. The method also includes executing a prediction algorithm, based on the state-space model, to generate predicted data from the anonymized data, and reconstructing the trajectory of the user or object using the predicted data. The method further includes generating a report indicative of the trajectory.

BACKGROUND

The present disclosure relates generally to privacy, and morespecifically to systems and methods for reconstructing a trajectory fromanonymized data.

The Global Positioning System (GPS), and other global navigationsatellite systems (GNSS), provide location information anywhere onEarth. Consumer devices, such smartphones, tablet computers, personaldigital assistants (PDAs), personal navigation devices (PNDs),in-vehicle navigation systems, vehicle control systems, advanced driverassistance systems (ADASs), and others, are increasingly adopting GPStechnologies. Such “probe” devices can generate a large pool of locationdata, including stay-points, check-ins, and mobility traces ortrajectories. When aggregated, location data can be used for trafficanalysis and prediction, fleet management, point-of-interestrecommendations, location-based services (LBS), and so on.

Data owners often collect and share location data, which may berepresented as sequences of time-stamped geographical coordinatescorresponding to user or probe device positions (i.e. “probe points”) asthey traverse various routes. Although such data publication can beuseful for urban planning, intelligent vehicles, logistics, and otherapplications, it risks revealing personal and sensitive information, andreduces control over how the data is used. For instance, mobility tracesor trajectories indicate the movement patterns of users, which canjeopardize their safety and security. As a result, various anonymizationmethods have been developed to help protect the identity of itscontributing users. However, conventional anonymization techniques arenaive, and often not sufficiently robust for safe publishing. Thisallows malicious adversaries to access information about contributingusers from the published data.

The level of sophistication of attacks aiming to exploit datavulnerabilities is growing. Accordingly, data owners, data aggregatorsand content providers face mounting challenges for protecting dataprivacy, and need improved approaches for evaluating the vulnerabilityof anonymized data.

SUMMARY

The present disclosure overcome the shortcomings of prior technologies.In particular, a novel approach for reconstructing a trajectory fromanonymized data is provided.

In accordance with aspect of the disclosure, a method for reconstructinga trajectory from anonymized data is provided. The method includesreceiving anonymized data corresponding to a trajectory of a user orobject along a road network, and assembling, based on the anonymizeddata, a state-space model having a state representation that correspondsto the road network. The method also includes executing a discreteprediction algorithm, based on the state-space model, to generatepredicted data from the anonymized data, and linking the predicted datato reconstruct the trajectory of the user or object. The method furtherincludes generating a report indicative of the trajectory.

In accordance with another aspect of the disclosure, a system forreconstructing a trajectory from anonymized data is provided. The systemincludes at least one processor, and at least one memory comprisinginstructions executable by the at least one processor, the instructionscausing the system to access anonymized data corresponding to atrajectory of a user or object along a road network, and assemble, basedon the anonymized data, a state-space model having a staterepresentation that corresponds to the road network. The instructionsalso cause the system to execute a discrete prediction algorithm, basedon the state-space model, to generate predicted data from the anonymizeddata, link the predicted data to reconstruct the trajectory of the useror object, and generate a report indicative of the trajectory. Thesystem further includes a display for providing the report.

In accordance with yet another aspect of the disclosure, anon-transitory computer-readable storage medium for reconstructing atrajectory from anonymized data is provided. The storage medium, carryone or more sequences of one or more instructions which, when executedby one or more processors, cause an apparatus to perform steps to accessanonymized data corresponding to a trajectory of a user or object alonga road network, and assemble, based on the anonymized data, astate-space model having a state representation that corresponds to theroad network. The instructions also cause the apparatus to execute adiscrete prediction algorithm, based on the state-space model, togenerate predicted data from the anonymized data, link the predicteddata to reconstruct the trajectory of the user or object, and generate areport indicative of the trajectory.

In accordance with yet another aspect of the disclosure, a method forreconstructing a trajectory from anonymized data is provided. The methodincludes receiving anonymized data corresponding to a trajectorytraversed by a user or object, and assembling a state-space model havinginternal states that represent geographical coordinates of thetrajectory. The method also includes, for a selected trajectory segmentassociated with the anonymized data, predicting future locations of theuser or object using the state-space model and particle filtering,reconstructing the trajectory using the future locations, and generatinga report indicative of the trajectory.

In accordance with another aspect of the disclosure, a system forreconstructing a trajectory from anonymized data is provided. The systemincludes at least one processor, and at least one memory comprisinginstructions executable by the at least one processor, the instructionscausing the system to receive anonymized data corresponding to atrajectory traversed by a user or object, and assemble a state-spacemodel having internal states that represent geographical coordinates ofthe trajectory. The instructions also cause the system to, for aselected trajectory segment associated with the anonymized data, predictfuture locations of the user or object using the state-space model andparticle filtering, reconstruct the trajectory using the futurelocations, and generate a report indicative of the trajectory. Thesystem further includes a display for providing the report.

In accordance with yet another aspect of the disclosure, anon-transitory computer-readable storage medium for reconstructing atrajectory from anonymized data is provided. The storage medium, carryone or more sequences of one or more instructions which, when executedby one or more processors, cause an apparatus to perform steps toreceive anonymized data corresponding to a trajectory traversed by auser or object, and assemble a state-space model having internal statesthat represent geographical coordinates of the trajectory. Theinstructions also cause the apparatus to, for a selected trajectorysegment associated with the anonymized data, predict future locations ofthe user or object using the state-space model and particle filtering,reconstruct the trajectory using the future locations, and generate areport indicative of the trajectory.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (or derived at least in part from)any one or any combination of methods (or processes) disclosed in thisapplication as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing a method of the claims.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with reference to theaccompanying figures, wherein like reference numerals denote likeelements. The embodiments of the invention are illustrated by way ofexample, and not by way of limitation, in the accompanying drawings:

FIG. 1 is an illustration demonstrating a split-gap anonymizationmethod, in accordance with aspects of the present disclosure.

FIG. 2 is a diagram of an example system, in accordance with aspects ofthe present disclosure;

FIG. 3 is a diagram of an example reconstruction engine of the system inFIG. 2.

FIGS. 4A-4C are graphical illustrations showing steps of a discreteprediction algorithm, in accordance with aspects of the presentdisclosure.

FIGS. 5A-5D are graphical illustrations showing steps of a continuousprediction algorithm, in accordance with aspects of the presentdisclosure.

FIG. 6 is a flowchart setting forth steps of a process, in accordancewith aspects of the present disclosure.

FIG. 7 is another flowchart setting forth steps of another process, inaccordance with aspects of the present disclosure.

FIG. 8 is a schematic diagram of a database, in accordance with aspectsof the present disclosure.

FIG. 9 is schematic diagram of an example computer system, in accordancewith aspects of the present disclosure.

FIG. 10 is a schematic diagram of an example chip set, in accordancewith aspects of the present disclosure.

DETAILED DESCRIPTION

Location data can be captured using various location-tracking devices(e.g. cell phones, tablets, personal and fitness trackers, vehiclenavigation devices, and so forth), and used to reveal patterns ofmovement or trajectories. For example, such patterns can benefit urbanplanners, government and regulatory bodies, fleet management services,and others. However, publication of user or device mobility has alwaysbeen challenging because of the sensitive nature of location data, andthe complexity of sound anonymization schemes to protect it.

To address this issue, some simple data anonymization methods have beendeveloped. One particular approach takes user trajectories, substitutesidentifications for the user trajectories with random pseudonyms, andreturns new trajectories in which user information has been removed.However, personally identifiable information is characteristic to thelocation data itself. That is, even in the absence of other information,a subset of location coordinates may be sufficient to identify a user.Thus, more advanced anonymization methods are required.

Another common anonymization method is split-gap anonymization. In thistechnique, as illustrated in FIG. 1, trajectory data (i.e. mobilitytrace data) is anonymized by splitting a given trajectory 10 intotrajectory segments 12, each including a sequence of geographicalcoordinates, and introducing gaps 14 by removing alternating trajectorysegments 12. The trajectory segments need not be equal in duration. Theremaining trajectory segments, 16′, 16″, . . . , 16 ^(n), are separatelyassigned individual identifications (IDs). In one example, a trajectorymay be anonymized by introducing gaps of approximately 2 minutes, andproducing trajectory segments between approximately 2 minutes and 10minutes in duration.

In principle, the split-gap approach can provide a good measure ofsecurity because linking together separated trajectory segments isdifficult, and reconstructing the original trajectory is even moredifficult. In practice, however, this anonymization scheme can fail,particularly in spatial regions and time periods where there isinsufficient data (e.g. corner and sparse trajectory cases). Hence,privacy may not necessarily be guaranteed by using this approach.

To ensure adequate data protection, it is important to evaluate theadequacy of anonymization technique. For instance, split-gapanonymization can be parameterized using two variables, namely splitcondition and gap condition. And so, determining which the parametervalues to use would help to achieve an optimal level of anonymizationusing this technique.

Conventionally, anonymization methods are evaluated by looking atindividual characteristics of the anonymized data (e.g. by sampling),and applying various heuristic rules to determine the quality of theanonymization. However, heuristic approaches are typically non-rigorous,and can be problematic when the data density becomes large. As a result,other approaches have been devised, including adversary models thatattempt to reconstruct anonymized data by modeling the actions ofattackers. For example, adversary models for split-gap anonymizationattempt to link back the separated trajectory segments of an anonymizedtrajectory by greedily stitching them together based on spatial distanceand time duration. However, many such adversary models can besimplistic, informal, vague or ambiguous.

To address these problems in the field of data privacy andanonymization, the present disclosure introduces a novel solution.Specifically, systems and methods are provided herein for reconstructinga trajectory from anonymized data. In the present approach, astate-space framework is utilized to predict the location of a user orobject (e.g. a user's device) at a future time point based a currentlocation and location history. The predictive scheme may be exact, orapproximate (e.g. based on sampling). Particularly with respect tosplit-gap anonymization, an anonymized trajectory may be reconstructedby repeatedly linking successive trajectory segments, for example, basedon predictive scores associated with the trajectory segments. Amongother applications, the success rate at which an anonymized data can bereconstructed using systems and methods described herein can be used toassess the quality or effectiveness of the anonymization and inform anyrisks or modifications required.

As appreciated from description below, the present disclosure affords anumber of advantages. For instance, exposing information about user'smobility is often undesirable due to privacy concerns. As such, thepresent approach provides a stronger attack model to predict user orobject mobility compared to conventional techniques. This allows formore reliable characterization and verification of protocols, systemsand schemes related to data anonymization. In addition, the approachdescribed is sufficiently flexible to include various information andclues, and integrate them into a unified model to improve the accuracyof prediction.

In what follows, and for the purposes of explanation, numerous specificdetails are set forth to provide a thorough understanding of theembodiments of the invention. It should be apparent to one skilled inthe art, however, that the embodiments of the invention may be practicedwith or without these specific details, or with equivalent arrangements.In other instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the embodimentsof the invention.

Referring particularly to FIG. 2, a schematic diagram of a system 100,in accordance with aspects of the present disclosure, is shown. Ingeneral, the system 100 may be any device, apparatus, system, or acombination thereof, that is configured to carry out steps forreconstructing a trajectory from anonymized data, in accordance withaspects of the present disclosure. The system 100 may include, be partof, or operate in collaboration with, various computers, systems,devices, machines, mainframes, networks, servers, databases, and soforth. In some embodiments, the system 100 may include portable ormobile devices, such as cellular phones, smartphones, laptops, tablets,and the like. In this regard, the system 100 may be designed tointegrate a variety of hardware, software, and firmware, implemented invarious forms and having various capabilities and functionalities. Inaddition, the system 100 may be capable of operating autonomously orsemi-autonomously.

As shown in FIG. 2, in some embodiments, the system 100 may include astate-space platform 101. The state-space platform 101 may be configuredto access, generate and process a variety of information and data, inaccordance with aspects of the present disclosure. In addition,state-space platform 101 may also communicate and exchangeinformation/data with various systems, devices and hardware. Forinstance, as shown in FIG. 2, the state-space platform 101 maycommunicate with one or more vehicle(s) 105, database(s) 107, userequipment (UE) 109, content provider(s) 111, and/or services platform(s)113 by way of a communication network 115.

To carry out steps, in accordance with aspects of the presentdisclosure, the state-space platform 101, and components therein, mayexecute instructions stored in a non-transitory computer-readable medium(not shown in FIG. 2). The non-transitory computer-readable medium maybe part of a memory, database, or other data storage location(s). Thestate-space platform 101, or components therein, may executeinstructions using a programmable processor, or combination ofprogrammable processors. Alternatively, or additionally, the state-spaceplatform 101 may also utilize one or more dedicated processors, orprocessing units, modules or systems specifically configured (e.g.hardwired, or pre-programmed) to carry out steps, in accordance withmethods described herein. In addition, the state-space platform 101 mayfurther include, as well as share, a variety of interconnectedcomponents, including servers, intelligent networking/computing devicesand other components, as well as corresponding software and/or firmware.By way of example, processing steps in accordance with aspects of thepresent disclosure may be carried out using any combination of centralprocessing units (CPUs), graphics processing units (GPUs), DigitalSignal Processing (DSP) chips, Application Specific Integrated Circuits(ASICs), Field Programmable Gate Arrays (FPGAs), and so forth.

In some embodiments, the state-space platform 101 may include areconstruction engine 103, as illustrated in FIG. 2. The reconstructionengine 103 may be configured to receive/access anonymized data (e.g.user or device locations, mobility traces, trajectories, etc), and applya state-space framework to the anonymized data to generate reconstructeddata. By way of example, the reconstructed data may be indicative oflocations, mobility traces, or trajectories of a user or object. Thereconstruction engine 103 may also be configured to generate and provideother information based on the reconstructed data. For example, thereconstruction engine 103 may be configured to generate and providefeedback indicative of the quality or efficiency of anonymization (e.g.the anonymization applied to the anonymized data). In addition, thereconstruction engine 103 may generate various confidencevalues/uncertainties (e.g. confidence or uncertainty in a predictedlocation or trajectory of a user or object).

Although the reconstruction engine 103 is shown as being part of thestate-space platform 101, it may be a stand-alone system or device.Alternatively, the reconstruction engine 103, or portions thereof, maybe integrated in the vehicle 105, UE 109, services platform 113 orservices 113 a-m, or a combination thereof.

As shown in FIG. 2, the state-space platform 101 may have connectivityor access to at least one database 107. Specifically, the database(s)107 may store a variety of data and information using various forms andformats. For instance, the database 107 may include device or probe data(e.g. geographical or location coordinates, timestamps, speed, heading,and so forth), road map data (e.g. network, geometry, class, free flow,average speed, and so forth), historical data (e.g. turn probabilities,speed profiles, and so forth). The database 107 may also include otherdata and information, including images or image data (e.g. terrestrialimages, aerial images, maps and so forth).

In addition, the state-space platform 101 may also communicate with UE109 and/or a vehicle 105. In one non-limiting example, the UE 109, oralternatively the vehicle 105, may execute an application 117 (e.g. asoftware application) configured to carry out steps in accordance withmethods described here. In another non-limiting example, application 117may also be any type of application that is executable on the UE 109and/or vehicle 105, such as autonomous driving applications, mappingapplications, location-based service applications, navigationapplications, content provisioning services, camera/imaging application,media player applications, social networking applications, calendarapplications, and the like. In yet another non-limiting example, theapplication 117 may act as a client for state-space platform 101, andperform one or more functions associated with reconstructing atrajectory from anonymized data, either alone or in combination with thestate-space platform 101.

By way of example, the UE 109 may be, or include, an embedded system,mobile terminal, fixed terminal, or portable terminal including abuilt-in navigation system, a personal navigation device, mobilehandset, station, unit, device, multimedia computer, multimedia tablet,Internet node, communicator, desktop computer, laptop computer, notebookcomputer, netbook computer, tablet computer, personal communicationsystem (PCS) device, personal digital assistants (PDAs), audio/videoplayer, digital camera/camcorder, positioning device, fitness device,television receiver, radio broadcast receiver, electronic book device,game device, or any combination thereof, including the accessories andperipherals of these devices, or any combination thereof. It is alsocontemplated that the UE 109 may support any type of interface with auser (e.g. by way of various buttons, touch screens, consoles, displays,speakers, “wearable” circuitry, and other I/O elements or devices).Although shown in FIG. 2 as being separate from the vehicle 105, in someembodiments, the UE 109 may be integrated into, or part of, the vehicle105.

In some embodiments, the UE 109 and/or vehicle 105 may include varioussensors for acquiring a variety of different data or information. Forinstance, the UE 109 and/or vehicle 105 may include one or morecamera/imaging devices for capturing imagery (e.g. terrestrial images),global positioning sensors (GPS) for gathering location or coordinatesdata, network detection sensors for detecting wireless signals,receivers for carrying out different short-range communications (e.g.,Bluetooth, Wi-Fi, Li-Fi, near field communication (NFC) etc.), temporalinformation sensors, audio recorders for gathering audio data, velocitysensors, switch sensors for determining whether one or more vehicleswitches are engaged, and others.

The UE 109 and/or vehicle 105 may also include light sensors, heightsensors and accelerometers (e.g., for determining acceleration andvehicle orientation), tilt sensors (e.g. for detecting the degree ofincline or decline), moisture sensors, pressure sensors, and so forth.Further, the UE 109 and/or vehicle 105 may also include sensors fordetecting the relative distance of the vehicle 105 from a lane orroadway, the presence of other vehicles, pedestrians, traffic lights,potholes, and any other objects, or a combination thereof. Other sensorsmay also be configured to detect weather data, traffic information, or acombination thereof. Yet other sensors may also be configured todetermine the status of various control elements of the car, such asactivation of wipers, use of a brake pedal, use of an accelerationpedal, angle of the steering wheel, activation of hazard lights,activation of head lights, and so forth.

In some embodiments, the UE 109 and/or vehicle 105 may include GPS orother satellite-based receivers configured to obtain geographicalcoordinates from a satellite 119 (see FIG. 2) for determining currentlocation and time. Further, the location can be determined by visualodometry, triangulation systems such as A-GPS, Cell of Origin, or otherlocation extrapolation technologies.

The state-space platform 101 may also have connectivity with variouscontent providers 111. Each content provider 111 a-111 n may send orprovide access to various information or data to the reconstructionengine 103, vehicle 105, database 107, user equipment 109, the servicesplatform 113, and any combination thereof. The content provided mayinclude map content (e.g., geographic data, parametric representationsof mapped features, and so forth), textual content, audio content, videoor image content (e.g. terrestrial image data), and so forth. In someimplementations, the providers 111 may exchange content with thestate-space platform 101, vehicle 105, database 107, UE 109, and/orservices platform 113. The content providers 111 may also manage accessto a central repository of data, and offer a consistent, standardinterface to data, such as a repository of the database 107.

As shown in FIG. 2, the state-space platform 101 may further connectover the communication network 115 to the services platform 113 (e.g. athird-party platform), which may provide one or more services 113 a-m.By way of example, the services platform 113 may provide mappingservices, navigation services, travel planning services, notificationservices, social networking services, content (e.g., audio, video,images, etc.) provisioning services, application services, storageservices, contextual information determination services, location basedservices, information based services (e.g., weather, news, etc.), and soforth. In one embodiment, the services platform 113 may use the outputof the reconstruction engine 103 (e.g., a predicted location ortrajectory) to localize the vehicle 105 or UE 109 (e.g., a portablenavigation device, smartphone, portable computer, tablet, etc.), andprovide services such as navigation, mapping, other location-basedservices, and so forth.

The communication network 115 may include any number of networks, suchas data networks, wireless networks, telephony networks, or combinationsthereof. It is contemplated that the data network may be any local areanetwork (LAN), metropolitan area network (MAN), wide area network (WAN),a public data network (e.g., the Internet), short range wirelessnetwork, or any other suitable packet-switched network, such as acommercially owned, proprietary packet-switched network, e.g., aproprietary cable or fiber-optic network, and the like, or anycombination thereof. In addition, the wireless network may be, forexample, a cellular network and may employ various technologiesincluding enhanced data rates for global evolution (EDGE), generalpacket radio service (GPRS), global system for mobile communications(GSM), Internet protocol multimedia subsystem (IMS), universal mobiletelecommunications system (UMTS), etc., as well as any other suitablewireless medium, e.g., worldwide interoperability for microwave access(WiMAX), Long Term Evolution (LTE) networks, code division multipleaccess (CDMA), wideband code division multiple access (WCDMA), wirelessfidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®, Internet Protocol(IP) data casting, satellite, mobile ad-hoc network (MANET), and thelike, or any combination thereof.

By way of example, the state-space platform 101, reconstruction engine103, vehicle 105, geographic database 107, UE 109, content provider 111,and services platform 113 may communicate with each other, and othercomponents of the system 100, using various communication protocols. Inthis context, a protocol may include a set of rules defining how thenetwork nodes within the communication network 115 interact with eachother based on information and data sent over the communication links.The protocols may be effective at different layers of operation withineach node, from generating and receiving physical signals of varioustypes, to selecting a link for transferring those signals, to the formatof information indicated by those signals, to identifying which softwareapplication executing on a computer system sends or receives theinformation. The conceptually different layers of protocols forexchanging information and data over a network are described in the OpenSystems Interconnection (OSI) Reference Model.

Communications between the network nodes may be carried out byexchanging discrete packets of data. Each packet may comprise (1) headerinformation associated with a particular protocol, and (2) payloadinformation that follows the header information and contains informationthat may be processed independently of that particular protocol. In someprotocols, the packet may include (3) trailer information following thepayload and indicating the end of the payload information. The headermay include information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. The data in the payload for the particular protocol mayinclude a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol may indicate a type for the next protocol containedin its payload. The higher layer protocol is said to be encapsulated inthe lower layer protocol. The headers included in a packet traversingmultiple heterogeneous networks, such as the Internet, may include aphysical (layer 1) header, a data-link (layer 2) header, an internetwork(layer 3) header and a transport (layer 4) header, and variousapplication (layer 5, layer 6 and layer 7) headers as defined by the OSIReference Model.

Referring now to FIG. 3, a schematic diagram of an examplereconstruction engine 103, in accordance with aspects of the presentdisclosure, is illustrated. In some embodiments, the reconstructionengine 103 may include various input/output (I/O) modules 201, aprediction module 203, a reconstruction module 205, a storage module207, and possibly others. The modules can be implemented using varioushardware, firmware, software, as described with reference to thestate-space platform 101 in FIG. 2. Alternatively, or additionally,modules may also be implemented as a cloud-based service, local service,native application, or combination thereof. Although not shown, thereconstruction engine 103 may also include various means ofcommunication between its respective modules, including variouscommunication hardware, buses, networks, and so forth.

The I/O modules 201 may include various input and output elements forreceiving and relaying various data and information. Example inputelements may include a mouse, keyboard, touchpad, touchscreen, buttons,and other user interfaces configured for receiving various selections,indications, and operational instructions from a user. Input elementsmay also include various drives and receptacles, such as flash-drives,USB drives, CD/DVD drives, and other computer-readable mediumreceptacles, for receiving various data and information. Example outputelements may include displays, touchscreens, speakers, LCDs, LEDs, andso on. In addition, I/O modules 201 may also include variouscommunication hardware configured for exchanging data and informationwith various external computers, systems, devices, machines, mainframes,servers or networks, for instance.

In some embodiments, as shown in the example of FIG. 3, thereconstruction engine 103 of FIG. 2 may include a prediction module 203and reconstruction module 205 configured to carry out steps, inaccordance with aspects of the present disclosure. Specifically, theprediction module 203 may carry out prediction algorithms (e.g. discreteor continuous) that receive or access anonymized data by way of the I/Omodules 201, and apply a state-space framework to generate predicteddata (e.g. location or trajectory at a future time point), as describedbelow. The reconstruction module 205 may then receive the predicteddata, and generate reconstructed data (e.g. user or object trajectory).In some aspects, the prediction module 203 and/or the reconstructionmodule 205 may be configured to generate scores based on probabilitiesfor transition between various trajectory segments, or portions thereof,of an anonymized trajectory, and link corresponding trajectory segmentsbased on their respective scores and durations. In some implementations,the probabilities of transition may be computed based on historicaldata, such as individual and aggregate information related to vehiclesor objects passing through the various trajectory segments, or portionsthereof. In one non-limiting example, the probabilities of transitionmay be computed based on the number of vehicles passing through a link,or taking a turn towards a link, in a given time period, and normalizedby the total of vehicles passing through the region associated with thatlink.

In some implementations, the reconstruction engine 103, by way of theprediction module 203, or reconstruction module 205, or both, maygenerate other data and information. For instance, the reconstructionengine 103 may generate and provide a report using the I/O modules 201.For instance, the report may include, various predictions for locations,trajectories or trajectory segments of a user or object, as well asconfidence values, uncertainties, or probability scores corresponding tosuch locations, trajectories or trajectory segments. In some aspects,the report may also indicate the quality or efficiency of dataanonymization. To this end, the reconstruction engine 103 may also beconfigured to compute the rate of success, accuracy, or other indexcharacterizing the reconstruction of the anonymized data. For example,raw data (e.g. trajectory data that is not anonymized) may selected tobe a “ground truth,” and anonymized using a split-gap technique. IDs maybe stored later for verification. The anonymized data may then beprocessed by the reconstruction engine 103, in accordance with methodsdescribed herein, to link the data back together. The rate of success ofthe reconstruction process may then be obtained by calculating thefraction of trajectory segments that are correctly reconstructed (asverified using ground truth labels) to the total number of trajectorysegments provided to the reconstruction engine 103.

Although the modules in FIG. 3 are shown as separate components of thereconstruction engine 103, it is contemplated that their respectivefunctions may be readily combined into fewer modules, or furtherseparated into more modules.

In general, state-space techniques attempt to model the evolution ofstates of a system based on a sequence of observations. The inherentstochasticity makes such approaches robust to noise and missinginformation. In recognizing these and other advantages of state-spacemodeling, the present disclosure introduces and applies a state-spaceframework for reconstructing a trajectory from anonymized data. In thisframework, the location coordinates, along with timestamps andoptionally additional metadata (e.g. speed, heading and so forth), of auser or object (e.g. a location-tracking device) may represent theinternal state, while the observations may represent anonymizedcoordinates (i.e. anonymized data) that are publicly available. Theobjective then becomes to estimate or predict the coordinates of theobject at a current or future time point based on the anonymizedcoordinates, as well as other observations. The present state-spaceframework is described in more detailed below.

State Representation:

The present disclosure envisions that a state-space model may representinternal state(s) in two different ways. A first variation may use adiscrete state representation. For instance, a road network may be usedto build a discrete set of states that are interconnected. The secondvariation may use a continuous state representation in which objectlocation may be modeled a real scale using geographical coordinates. Asappreciated from description below, the representation of states candetermine the prediction accuracy and the complexity of the model. Forinstance, a large number of states might provide a precise location, butwould increase time to make the prediction. Also, a larger number ofstates might require more historical data to achieve bettergeneralization capabilities.

In some aspects, the prediction accuracy may be increased by integratingother information/data and clues into the state-space models. Forexample, such information may include probe properties (e.g. location orgeographical coordinates, timestamps, speed, heading, and so forth),road map properties (e.g. network, geometry, class, free flow, averagespeed, and so forth), historical dataset properties (e.g. turnprobabilities, speed profiles, and so forth), anonymization technique(e.g. gap duration, split duration, and so forth), and otherinformation. This may be achieved by considering such information aspart of the observation.

Prediction:

The prediction algorithms described herein may use the staterepresentation to assign a probability score to each of the possiblestates, indicating how likely it is for a tracked object to be in suchstates. Since the prediction algorithms, discrete or continuous, aredirectly tied to the underlying state representation, they are describedtogether in the following.

Discrete Network Based State and Enumerated Prediction:

In some aspects, the state representation may use an underlying roadnetwork, for example, modeled as a directed graph or using a map. In adirected graph, roads correspond to edges or links, and intersectionscorrespond to vertices or nodes. In some aspects, the directed graph maybe represented as a matrix (e.g. a sparse adjacency matrix) with nodesas rows, and columns as edges. For each trajectory segment of ananonymized trajectory, or a portions thereof, the discrete predictionalgorithm may generate and utilize probability scores to identify thenext likely trajectory segment, or portion thereof, along the roadnetwork. Computed probability scores may correspond to a likelihood oftransitioning between two or more links along the road network. Forinstance, a probability score may represent the likelihood of reachingone link (e.g. link B) from another link (e.g. link A) after apredetermined duration or at a future time point. In some aspects, asequence of transitions between various links along a path may be usedto calculate the likelihood. In one non-limiting example, for a pathA-B-C that includes links A, B, and C arranged in succession, theprobability of transition between links A and B, and the probability oftransition between links B and C, may be multiplied together todetermine the likelihood of transition between links A and C. In anothernon-limiting example, two paths may connect links W and Z, namely bypath W-X-Z and by path W-Y-Z, In this case, the aggregate likelihood oftransition between link W and link Z may be computed by adding thelikelihood along path W-X-Z and the likelihood along path W-Y-Z, eachlikelihood computed as described above. Of course, paths with fewer ormore links, and arranged in various configurations may be possible.Also, this approach may be used to model the possibility of travelingbetween roads connected by an intersection. The probability scores maybe normalized across all possible roads, or road links, connected tothat intersection. In some implementations, the speed profile of eachroad in the road network may be stored and accessed. The speed profilemay include maximum speed, free flow speed, average speed across timeand with respect to discrete time intervals. In some aspects, thetransition probabilities and speed profiles may be estimated fromhistorical data and other data sources, such as speed lookups. Theseparameters can then provide an estimate for the probability of reachinga point B from a point A on a road network.

A discrete prediction algorithm, in accordance with aspects of thepresent disclosure, is illustrated in the example of FIGS. 4A-4C.Specifically, the algorithm may begin by receiving or accessinganonymized data (e.g. trajectory data) of a user or object, and matchingthe data to a map. This is because trajectory segments, which ofteninclude noisy observations (i.e. geographical coordinates), might notnecessarily have a one-to-one correspondence to specific links on agiven map. And so, each trajectory segment can be matched to theircorresponding links. With specific reference to the example shown inFIG. 4A, the prediction algorithm map matches a trajectory segment 402to a road network 400 to obtain the links in the road network 400 towhich the trajectory segment 402 corresponds. The algorithm may alsoobtain a future time point for which the prediction would be carried outby the algorithm. In some aspects, the future time point may be providedby a user or pre-defined. For example, if the gap interval variable ofthe anonymization is 2 minutes, then +2 minutes may be selected ordefined to be the future time point. Then the algorithm may obtain amaximum speed based on the speed profile of the user or object. Thespeed profile may include speeds along a current link, or speeds alonglinks adjacent to the current link, or speeds throughout the trajectorysegment 402 or other portions of the trajectory. To optimize predictionaccuracy, the maximum speed may be selected based on the highest speedin the speed profile.

The maximum distance reachable by the object may then be computed basedon the maximum speed and duration between the last known timestamp ofthe object along the trajectory segment 402, and a future timestamp(e.g. future time point for which the prediction is carried out). Suchmaximum distance can then be used to define a region 404 in the roadnetwork 400 that is accessible by object at the point in time reflectedby future timestamp, as shown in FIG. 4B.

The prediction algorithm may then identify and filter out links 406 thatfall within the region 404 of the road network 400. To this end, asearch and scoring of the links 406 may be performed by the algorithm.In some implementations, the search may be incremental, beginning withlinks immediate to the last known timestamp, followed by links connectedthereto, and so forth. Link scoring may be based on several factors,including transition probabilities between the links 406, and durationsof the links 406. For instance, for given a link within the region 404,a transition probability represents the probability of transitioning tothe next link in the region 404, while the duration is the expected timespent in the link (e.g. based on the speed profile and distanceassociated with the link). In some implementations, the transitionprobabilities are computed using historical data. In scoring each linkin the region 404, an accumulated transition probability is computed bymultiplying probabilities of transition between links 406 along the pathfrom the origin link 408 (FIG. 4B) to the link. In some aspects, anaccumulated duration may also be computed for each respective link bysumming durations along the path from the origin link 408 to the link. Aprobability score may then be calculated for each link by multiplyingtogether the accumulated transition probability and accumulatedduration. Alternatively, or additionally, the score may be based only onthe accumulated transition probability.

By sorting probability scores corresponding to each of the links 406,and filtering the links 406 based on the scores, the most probable pathfollowing the trajectory segment 402, namely the next trajectory segment410, may be obtained or synthesized. In some aspects, the most probablepath for a user or object may be computed by multiplying the transitionprobabilities at every intersection within the region 404, and rankingbased on the product. For example, given a link at an intersection inthe region 404, transition probabilities for each link connected to thatlink are multiplied together. The most probably path may then follow themaximum product of transition probabilities.

Continuous State and Sampling-Based Prediction:

As described, a continuous state may be used to represent a predictedlocation in terms of geographical coordinates in real scale. Apredictive algorithm based on a particle filtering approach may then beexecuted to identify future locations of the user or object, ordetermine missing points or segment between adjacent trajectories, ortrajectory segment (as shown in FIG. 5A). To note, and unlike thediscrete approach, geographical coordinates in real scale may not beefficiently and exhaustively searched. Hence, in some aspects, asampling-based approach may be utilized to provide improved efficiency(e.g. sequential Monte Carlo family of methods). For example, aSequential Importance Resampling (SIR), which is special case ofimportance sampling, may be used. In particular, particle filters use aset of sampled points, potentially closer state to the object and trackthem at each step by iteratively optimizing the importance weightsassigned to these particles. The internal state being maintained is thelocation geographical coordinates of the user or object along withauxiliary variables, such as speed and heading.

A continuous prediction algorithm, in accordance with aspects of thepresent disclosure, is illustrated in FIGS. 5A-5D. As described above,an anonymized trajectory along a road network 500 may include a firsttrajectory segment 502 and a second trajectory segment 504 separated bya gap, each segment including a sequence of geographical coordinates orobservation points (FIG. 5A). To reconstruct the trajectory, theprediction algorithm may begin by randomly initializing a set of samplepoints, or particles 506, as internal states having geographicalcoordinates close to the start location of the first trajectory segment502 (FIG. 5B). For example, the particles 506 may be within apredetermined distance from the observation point that is closest tostart location of the first trajectory segment 502. The particles 506may also be assigned weights (e.g. equal weights) reflecting theirrespective importance. For instance, such assigned weights may signifyhow close they reflect an observation, and may also be used to ‘retain’the point for future time steps, as described below.

Then, for each observed point in the first trajectory segment 502, atransition 508 may be applied to predict the next observation point,which may also be the start of the second trajectory segment 504 or anew trajectory segment. That is, each observation point, correspondingto an initial state of a particle 506, may be used to predict a futurestate of the respective particle 506 (e.g. represented by geographicalcoordinates and timestamp), and such predictions may be performed foreach of the particles 506. In some implementations, the transition 508may be represented using a motion model. For instance, the transition508 may be based on a linear model, which determines a next location asa linear function of a previous location based on a speed/heading andduration. (FIG. 5C). As a non-limiting example, a simple linear modelmay be used, namely: p₁=p₀+s*t, where p₀ is a point at time point 0, p₁is a point at time point 1, s is the speed, and t is the time duration.The model then predicts how far a user or object would reach bytraveling at speed s for time duration t. The transition 508 may bebased, additionally or alternatively, on more advanced, non-linearmodels. To note, observations can often include errors, for instance,due to the noise in GPS readings or interference. Therefore, in someaspects, noise may be added to the particles 506 during prediction inorder to mimic such errors. For example, random noise from a selecteddistribution may be added.

Using the observations in the first trajectory segment 502, the weightsof each of the particles 506 may then be updated. A normalization mayalso be carried across the particles 506 to update the weights. Theparticles 506 may then be resampled based on the updated weights toproduce resampled particles 510 (FIG. 5D). Unlikely particles 506 may beremoved, ensuring that the resampled particles 510 closer to the actualobservation are boosted and far-enough particles are withered-off. Theresampled particles 510 may then be used to predict the location of auser or object at a future time point (e.g. determined by a gapduration), or along a trajectory or trajectory segment. In some aspects,the resampled particles 510 are weight-averaged to obtain theprediction.

Linking Trajectory Segment:

As described, split gap-based anonymization takes a trajectory andbreaks it up into different trajectory segment by introducing gaps inthe trajectory data. Linking trajectory segment, in accordance withaspects of the disclosure, allows for the reconstruction of suchanonymized trajectory. To this end, the present prediction algorithmsmay, in some aspects, utilize a simple greedy approach. For instance,given an anonymized dataset that includes n trajectory segment, thereare n*(n−1)/2 possible pairs of trajectory segments. However, many ofthese combinations may be eliminated based on certain constraints, suchas timestamp, speed and location constraints. For example, a trajectorysegment starting in the morning is very likely not directly related toanother trajectory segment that starts in the evening. Similarly,trajectory segments belonging to different regions in a city that arefar enough from one another are also likely not directly related. Onceunrelated pairs are eliminated as possibilities, the remaining pairs oftrajectory segments are then scored individually, as described above,and sorted. In some aspects, scores may be based on probabilities fortransitioning between the end of one trajectory segment and thebeginning of another trajectory segment. The pairs of trajectorysegments may then be linked based on their respective scores anddurations. In particular, the durations may be between the last point ofa first trajectory segment and the first point of the second trajectorysegment. In the case of particle filtering, scoring may be done byconsidering two trajectory segments as a single segment, and computingthe likelihood for such single segment. In particular, using particlefilter, the likelihood of particles starting from end of a firsttrajectory segment to reach the start of the second trajectory segmentmay be computed. This enhances the accuracy of the sampling-basedestimate.

In some methods of anonymization, such as split-gap anonymization, a gapis introduced into a trajectory to form different trajectory segments,and the duration of such gap may be known. For instance, the gapduration may be exact or an interval. In such cases, pairs of trajectorysegments may be linked if their duration is equal to, or similar to, thegap duration.

Trajectory Reconstruction:

As described, anonymized trajectories may be reconstructed by linkingtogether trajectory segments separated, for example, by a split-gapanonymizer. The linking process may begin by generating a synthesizedtrajectory from two trajectory segments using their respective scores(e.g. accumulated scores). The process may then continue by linkingadditional trajectory segments to the end of the synthesized trajectory,until one or more predetermined conditions or constraints are satisfied.For example, the linking process may continue until the entireanonymized dataset is processed. To note, in some cases, it may notpossible to process the entire anonymized dataset, owing to datasparsity. And so, in some aspects, the linking process may stop when thedata becomes insufficient or sparse.

Alternatively, or additionally, the linking process may stop or continuebased on a predetermined threshold. For instance, in someimplementations, the linking process may be controlled by keeping trackof a cumulative score for the synthesized trajectory. In particular,such cumulative score may be computed by summing the individual scoresassociated with each of the trajectory segments forming the synthesizedtrajectory. The linking process may then stop when the cumulative scoreis above, equal to, or below a predetermined value.

In some implementations, a minimum description length (MDL) may bealternatively, or additionally, used to control the linking process. MDLis a theoretical measure of information, which can be defined as theamount of information required to describe a set of observations given amodel that describes the observations along with the model. Hence it isa sum that depends on two factors, namely the likelihood of observationsfollowing a model and the model complexity. As described, the presentstate-space model may be governed by the transition probabilities usedto score the trajectory segments, and model complexity increases withevery segment trajectory being appended to the synthesized trajectory.Hence considering the average log-likelihood of trajectory segments thatare being stitched (ΣL(t₁, t₂)/n) as a criterion is a good. Intuitively,it allows assembly of the synthesized trajectory until the incrementalincrease in complexity is justified by a lower likelihood score.

In this case, the synthesized trajectory may be assembled byinitializing an MDL score M₀ to zero. At stitching step i, the highestscoring trajectory segment is linked to the and synthesized trajectoryS. An updated MDL score, M_(i), may be computed using: M_(i)=(L(S,T_(i))+M_(i−1))/i. If the updated MDL score is greater than a threshold,namely M_(i)>M_(thres), the process proceeds to the next iteration, i+1.Otherwise, the linking process is stopped, and the synthesizedtrajectory is returned, for example, in a report.

Referring particularly to FIG. 6, a flowchart setting forth steps of aprocess 600, in accordance with aspects of the present disclosure, isshown. Steps of the process 600 may be carried out using any combinationof suitable devices or systems, as well as using systems described inthe present disclosure. In some embodiments, steps of the process 600may be implemented as instructions stored in non-transitory computerreadable media, as a program, firmware or software, and executed by ageneral-purpose, programmed or programmable computer, processer or othercomputing device. In other embodiments, steps of the process 600 may behardwired in an application-specific computer, processer, dedicatedsystem, or module, as described with reference to FIGS. 2 and 3.Although the process 600 is illustrated and described as a sequence ofsteps, it is contemplated that the steps may be performed in any orderor combination, and need not include all of the illustrated steps.

The process 600 may begin at process block 602 with receiving anonymizeddata corresponding to a trajectory of a user or object along a roadnetwork. As described, the anonymized data may include location datathat is anonymized (e.g. using a split-gap anonymization technique) toprotect user or object privacy. The data may be provided by or accessedfrom, for example, a database 107, a vehicle 105, or a content provider111, as described with reference to FIG. 2, as well as from elsewhere(e.g. a memory, server, and so forth). Upon receipt, the data may beprocessed in any number of ways. For instance, the anonymized data maybe map matched to a road network, where the map may include a number oflinks and nodes, as well as other attributes or features.

Then, at process block 604, a state-space model may be assembled basedon the anonymized data. As described, in some aspects, the state-spacemodel may have a state representation that corresponds to the roadnetwork to which the anonymized data is map matched. Based on theassembled state-space model, a discrete prediction algorithm may then beexecuted to generate predicted data from the anonymized data, asindicated by process block 606.

The discrete prediction algorithm may generate the predicted data (e.g.a subsequent trajectory segment) by determining a maximum distance thatis reachable from a given trajectory segment in the anonymized data. Asdescribed, the maximum distance may be determined using the speedprofile of the user or object and a predetermined future time point. Thealgorithm may also use the anonymized data to generate probabilityscores, where the probability scores correspond to the likelihood oftransition between different links on the road network. In some aspects,the probability scores may be estimated by using a combination of probedata, road map data, and historical data. Furthermore, the algorithm mayfilter out links within a region of the road network defined by themaximum distance.

The predicted data may then be linked to reconstruct the trajectory ofthe user or object, as indicated by process block 608. As described,linking trajectory segments may be based on certain constraints, such astimestamp, speed and location constraints. Once unrelated pairs areeliminated as possibilities, the remaining pairs of trajectory segmentsare then scored individually and sorted. The pairs of trajectorysegments may then be linked based on their respective scores and theirrespective durations. In some aspects, reconstruction may be performedbased on cumulative score for the trajectory being linked.Reconstruction may be performed iteratively until a predeterminedthreshold is reached or a condition is met.

A report may then be generated, as indicated by process block 610. Thereport may be in any form, and provide various information. In someimplementations, the report may be in the form of visual and/or audiosignals, images, tabulated information and data, instructions, andcombinations thereof. The report may be communicated to a user oroperator by way of a display, speakers, or other means of output, adatabase, as well as to various devices or systems for further steps,analysis or processing. In some aspects, the report may be provided inreal-time (e.g. in a time substantially corresponding to the time dataof data capture and/or processing). The report, and various data andinformation therein, may also be stored (e.g. in a memory, a database, aserver, and so forth). In some aspects, data and information provided inthe report may be used to control mapping information inaccuracies. Thatis, based on the quality of terrestrial data, various mappinginformation derived therefrom may be appropriately considered (e.g.updated, corrected, and so forth).

For instance, the report may include, various predictions for locations,trajectories or trajectory segments of a user or object, as well asconfidence values, uncertainties, or probability scores corresponding tosuch locations, trajectories or trajectory segments. In some aspects,the report may also indicate the quality or efficiency of dataanonymization by way of various indices or metrics indicative of, forexample, the rate of success or accuracy for reconstructing theanonymized data.

Turning now to FIG. 7, another flowchart setting forth steps of aprocess 700, in accordance with aspects of the present disclosure, isshown. As described above, steps of the process 700 may be carried outusing any combination of suitable devices or systems, as well as usingsystems described in the present disclosure. In some embodiments, stepsof the process 700 may be implemented as instructions stored innon-transitory computer readable media, as a program, firmware orsoftware, and executed by a general-purpose, programmed or programmablecomputer, processer or other computing device. In other embodiments,steps of the process 700 may be hardwired in an application-specificcomputer, processer, dedicated system, or module, as described withreference to FIGS. 2 and 3. Although the process 700 is illustrated anddescribed as a sequence of steps, it is contemplated that the steps maybe performed in any order or combination, and need not include all ofthe illustrated steps.

The process 700 may begin at process block 702 with receiving anonymizeddata corresponding to a trajectory traversed by a user or object. Insome aspects, the trajectory is traversed along a road network. Asdescribed, anonymized data may include location data that is anonymizedto protect user or object privacy. In some aspects, the data may beanonymized by dividing the trajectory traversed into a number oftrajectory segments, and introducing one or more predefined gaps byremoving some of the trajectory segments. The data may be provided by oraccessed from, for example, a database 107, a vehicle 105, or a contentprovider 111, as described with reference to FIG. 2, as well as fromelsewhere (e.g. a memory, server, and so forth). Upon receipt, the datamay be processed in any number of ways.

Then, at process block 704, a state-space model may be assembled basedon the anonymized data. As described, the state-space model may haveinternal states that represent geographical coordinates of thetrajectory. Based on the assembled state-space model, future locationsof a user or object may then be predicted for selected trajectorysegments associated with the anonymized data, as indicated by processblock 706.

As described, this step may include executing a continuous predictionalgorithm which randomly initializes a set of particles corresponding toa selected trajectory segment (e.g. FIG. 5B). Initial weightscorresponding to the particles may then be assigned, and a transitionapplied (e.g. FIG. 5C). In some aspects, the transition may berepresented using a linear model or a non-linear model. Onceinitialized, the weights of the set of particles may be updated, and theparticles resampled based on the updated weights. The resampledparticles may then be used to predict a future location of the user orobject at a future time point. The step may be repeated a number oftimes to predict multiple future locations of the user or object. Insome aspects, a sampling-based approach may be used, such as the SIR.

Then, at process block 708, the trajectory may then be reconstructedbased on the determined future locations. To this end, a linking processmay be carried out to generate a synthesized trajectory representing thereconstructed trajectory. As described, the process may begin by linkingtwo trajectory segments, iteratively adding additional trajectorysegments to the synthesized trajectory until a predetermined thresholdis reached or a condition is met. In some aspects, linking trajectorysegments may be based certain constraints, such as timestamp, speed andlocation constraints, as well as probability scores, as described.

A report may then be generated, as indicated by process block 610. Thereport may be in any form, and provide various information. In someimplementations, the report may be in the form of visual and/or audiosignals, images, tabulated information and data, instructions, andcombinations thereof. The report may be communicated to a user oroperator by way of a display, speakers, or other means of output, adatabase, as well as to various devices or systems for further steps,analysis or processing. In some aspects, the report may be provided inreal-time (e.g. in a time substantially corresponding to the time dataof data capture and/or processing). The report, and various data andinformation therein, may also be stored (e.g. in a memory, a database, aserver, and so forth). In some aspects, data and information provided inthe report may be used to control mapping information inaccuracies. Thatis, based on the quality of terrestrial data, various mappinginformation derived therefrom may be appropriately considered (e.g.updated, corrected, and so forth).

For instance, the report may include, various predictions for locations,trajectories or trajectory segments of a user or object, as well asconfidence values, uncertainties, or probability scores corresponding tosuch locations, trajectories or trajectory segments. In some aspects,the report may also indicate the quality or efficiency of dataanonymization by way of various indices or metrics indicative of, forexample, the rate of success or accuracy for reconstructing theanonymized data.

Turning now to FIG. 8, a diagram of a database 107, according to aspectsof the present disclosure, is shown. As shown, the database 107 mayinclude a variety of geographic data 801 tabulated in variousarrangements, and used in various applications. For example, thegeographic data 801 may be used for (or configured to be compiled to beused for) mapping and/or navigation-related services. As shown in FIG.8, the geographic data 801 may include node data records 803, roadsegment data records 805, point of interest (POI) data records 807,point data records 809, high definition (HD) mapping data records 811,and indexes 813, for example. The geographic data8 may include more,fewer or different data records. In some embodiments, additional datarecords not shown in FIG. 8 may also be included, such as cartographic(“carto”) data records, routing data records, maneuver data records, andother data records.

In particular, the HD mapping data records 811 may include a variety ofdata, including data with resolution sufficient to providecentimeter-level or better accuracy of map features. For example, the HDmapping data may include data captured using LiDAR, or equivalenttechnology capable large numbers of 3D points, and modeling roadsurfaces and other map features down to the number lanes and theirwidths. In one embodiment, the HD mapping data (e.g., HD data records811) capture and store details such as the slope and curvature of theroad, lane markings, roadside objects such as sign posts, including whatthe signage denotes. By way of example, the HD mapping data enablehighly automated vehicles to precisely localize themselves on the road.

In some implementations, geographic features (e.g., two-dimensional orthree-dimensional features) may be represented in the database 107 usingpolygons (e.g., two-dimensional features) or polygon extrusions (e.g.,three-dimensional features). For example, the edges of the polygonscorrespond to the boundaries or edges of the respective geographicfeature. In the case of a building, a two-dimensional polygon can beused to represent a footprint of the building, and a three-dimensionalpolygon extrusion can be used to represent the three-dimensionalsurfaces of the building. It is contemplated that although variousembodiments are discussed with respect to two-dimensional polygons, itis contemplated that the embodiments are also applicable tothree-dimensional polygon extrusions. Accordingly, the terms polygonsand polygon extrusions as used herein can be used interchangeably.

In one embodiment, the following terminology applies to therepresentation of geographic features in the database 107:

“Node”—A point that terminates a link.

“Line segment”—A straight line connecting two points.

“Link” (or “edge”)—A contiguous, non-branching string of one or moreline segments terminating in a node at each end.

“Shape point”—A point along a link between two nodes (e.g., used toalter a shape of the link without defining new nodes).

“Oriented link”—A link that has a starting node (referred to as the“reference node”) and an ending node (referred to as the “non referencenode”).

“Simple polygon”—An interior area of an outer boundary formed by astring of oriented links that begins and ends in one node. In oneembodiment, a simple polygon does not cross itself.

“Polygon”—An area bounded by an outer boundary and none or at least oneinterior boundary (e.g., a hole or island). In one embodiment, a polygonis constructed from one outer simple polygon and none or at least oneinner simple polygon. A polygon is simple if it just consists of onesimple polygon, or complex if it has at least one inner simple polygon.

In some implementations, certain conventions or rules may be followed inthe database 107. For example, links may not cross themselves or eachother except at a node. In another example, shape points, nodes, orlinks may not be duplicated. In yet another example, two links thatconnect each other may have a common node. In the database 107,overlapping geographic features are represented by overlapping polygons.When polygons overlap, the boundary of one polygon crosses the boundaryof the other polygon.

In the database 107, the location at which the boundary of one polygonintersects the boundary of another polygon may be represented by a node.In one embodiment, a node may be used to represent other locations alongthe boundary of a polygon than a location at which the boundary of thepolygon intersects the boundary of another polygon. In one embodiment, ashape point may not be used to represent a point at which the boundaryof a polygon intersects the boundary of another polygon.

In exemplary embodiments, the road segment data records 805 may be linksor segments representing roads, streets, or paths, as can be used in thecalculated route or recorded route information for determination of oneor more personalized routes. The node data records 803 may be end pointscorresponding to the respective links or segments of the road segmentdata records 805. The road link data records 805 and the node datarecords 803 may represent a road network, as used by vehicles, cars,and/or other entities, for instance. Alternatively, the database 107 maycontain path segment and node data records or other data that representpedestrian paths or areas in addition to or instead of the vehicle roadrecord data, for example.

The road/link segments and nodes can be associated with attributes, suchas functional class, a road elevation, a speed category, a presence orabsence of road features, geographical coordinates, street names,address ranges, speed limits, turn restrictions at intersections, andother navigation related attributes, as well as POIs, such as gasolinestations, hotels, restaurants, museums, stadiums, offices, automobiledealerships, auto repair shops, buildings, stores, parks, etc. Thedatabase 107 can include data about the POIs and their respectivelocations in the POI data records 807. The database 107 can also includedata about places, such as cities, towns, or other communities, andother geographic features, such as bodies of water, mountain ranges,etc. Such place or feature data can be part of the POI data records 807or can be associated with POIs or POI data records 807 (such as a datapoint used for displaying or representing a position of a city).

As shown in FIG. 8, the database 107 may also include point data records809 for storing the point data, learnable map features, as well as otherrelated data used according to the various embodiments described herein.In addition, the point data records 809 can also store ground truthtraining and evaluation data, machine learning models, annotatedobservations, and/or any other data. By way of example, the point datarecords 809 can be associated with one or more of the node records 803,road segment records 805, and/or POI data records 807 to supportlocalization or visual odometry based on the features stored therein andthe corresponding estimated quality of the features. In this way, therecords 809 can also be associated with or used to classify thecharacteristics or metadata of the corresponding records 803, 805,and/or 807.

As discussed above, the HD mapping data records 811 may models of roadsurfaces and other map features to centimeter-level or better accuracy.The HD mapping data records 811 may also include models that provide theprecise lane geometry with lane boundaries, as well as rich attributesof the lane models. These rich attributes may include, but are notlimited to, lane traversal information, lane types, lane marking types,lane level speed limit information, and/or the like. In one embodiment,the HD mapping data records 811 may be divided into spatial partitionsof varying sizes to provide HD mapping data to vehicles and other enduser devices with near real-time speed without overloading the availableresources of these vehicles and devices (e.g., computational, memory,bandwidth, etc. resources).

In some implementations, the HD mapping data records 811 may be createdfrom high-resolution 3D mesh or point-cloud data generated, forinstance, from LiDAR-equipped vehicles. The 3D mesh or point-cloud datamay be processed to create 3D representations of a street or geographicenvironment at centimeter-level accuracy for storage in the HD mappingdata records 511.

In one embodiment, the HD mapping data records 811 also includereal-time sensor data collected from probe vehicles in the field. Thereal-time sensor data, for instance, integrates real-time trafficinformation, weather, and road conditions (e.g., potholes, roadfriction, road wear, etc.) with highly detailed 3D representations ofstreet and geographic features to provide precise real-time also atcentimeter-level accuracy. Other sensor data can include vehicletelemetry or operational data such as windshield wiper activation state,braking state, steering angle, accelerator position, and/or the like.

The database 107 may be maintained by content provider in associationwith a services platform (e.g., a map developer), as described withreference to FIG. 2. The map developer can collect data to generate andenhance the database 107. The data may be collected in various ways bythe map developer, including obtaining data from other sources, such asmunicipalities or respective geographic authorities. In addition, themap developer can employ field personnel to travel by vehicle alongroads throughout the geographic area of interest to observe featuresand/or record information about them, for example. Also, remote sensing,such as aerial or satellite photography, can be used.

In some implementations, the database 107 can be a master geographicdatabase stored in a format that facilitates updating, maintenance, anddevelopment. For example, the master geographic database or data in themaster geographic database can be in an Oracle spatial format or otherspatial format, such as for development or production purposes. TheOracle spatial format or development/production database can be compiledinto a delivery format, such as a geographic data files (GDF) format.The data in the production and/or delivery formats can be compiled orfurther compiled to form geographic database products or databases,which can be used in end user navigation devices or systems.

For example, data may be compiled (such as into a platform specificationformat (PSF) format) to organize and/or configure the data forperforming navigation-related functions and/or services, such as routecalculation, route guidance, map display, speed calculation, distanceand travel time functions, and other functions, by a navigation deviceof a vehicle, for example. The navigation-related functions cancorrespond to vehicle navigation, pedestrian navigation, or other typesof navigation. The compilation to produce the end user databases can beperformed by a party or entity separate from the map developer. Forexample, a customer of the map developer, such as a navigation devicedeveloper or other end user device developer, can perform compilation ona received geographic database in a delivery format to produce one ormore compiled navigation databases.

The indexes 813 in FIG. 8 may be used improve the speed of dataretrieval operations in the database 107. Specifically, the indexes 813may be used to quickly locate data without having to search every row inthe geographic database 107 every time it is accessed. For example, inone embodiment, the indexes 813 can be a spatial index of the polygonpoints associated with stored feature polygons.

An example computer system 900, in accordance with aspects of thepresent disclosure, is illustrated in FIG. 9. The computer system 900may be programmed (e.g., via computer program code or instructions) toperform a variety of steps, including steps for reconstructing atrajectory from anonymized data, in accordance with methods describedherein.

As shown in FIG. 9, the computer system 900 may generally include aprocessor 902, which may be configured to perform a set of operations oninformation as specified by computer program code. The computer programcode is a set of instructions or statements providing instructions forthe operation of the processor and/or the computer system to performspecified functions. The code, for example, may be written in a computerprogramming language that is compiled into a native instruction set ofthe processor. The code may also be written directly using the nativeinstruction set (e.g., machine language). In some aspects, the set ofoperations may include bringing information in from a bus 910 andplacing information on the bus 910. The set of operations may alsoinclude comparing two or more units of information, shifting positionsof units of information, and combining two or more units of information,such as by addition or multiplication or logical operations like OR,exclusive OR (XOR), and AND. Each operation of the set of operationsperformed by the processor 902 may be represented to the processor 902by information called instructions, such as an operation code of one ormore digits. The sequence of operations to be executed by the processor902, such as a sequence of operation codes, constitute processor 902instructions, may also be called computer system 900 instructions or,simply, computer instructions. The processor 902 may include multipleprocessors, units or modules, and may be implemented as mechanical,electrical, magnetic, optical, chemical or quantum components, amongothers, or any combination thereof.

As shown in FIG. 9, the computer system 900 may also include a memory904 coupled to bus 910. The memory 904, such as a random-access memory(RAM) or other dynamic storage device, may be configured to store avariety of information and data, including processor instructions forcarrying steps in accordance with aspects of the disclosure. Dynamicmemory allows information stored therein to be changed by the computersystem 900. The RAM allows a unit of information stored at a locationcalled a memory address to be stored and retrieved independently ofinformation at neighboring addresses. The memory 904 may also be used bythe processor 902 to store temporary values during execution ofprocessor instructions.

The computer system 900 may also include a read-only memory (ROM) 906,or other static storage device, coupled to the bus 910. The ROM 906 maybe configured for storing static information, including instructions,that is not changed by the computer system 900. Some memory 904 includesvolatile storage that loses the information stored thereon when power islost. Also coupled to bus 910 is a non-volatile (persistent) storagedevice 908, such as a magnetic disk, optical disk or flash card, forstoring information, including instructions, that persists even when thecomputer system 900 is turned off or otherwise loses power.

As mentioned, the bus 910 may be configured for passing information anddata between internal and external components of the computer system900. To do so, the bus 910 may include one or more parallel conductorsthat facilitate quick transfer of information and data among thecomponents coupled to the bus 910. The information and data may berepresented as a physical expression of a measurable phenomenon,typically electric voltages, but including, in other embodiments, suchphenomena as magnetic, electromagnetic, pressure, chemical, biological,molecular, atomic, sub-atomic and quantum interactions. For example,north and south magnetic fields, or a zero and non-zero electricvoltage, may represent two states (0, 1) of a binary digit (bit). Otherphenomena can represent digits of a higher base. A superposition ofmultiple simultaneous quantum states before measurement represents aquantum bit (qubit). A sequence of one or more digits constitutesdigital data that is used to represent a number or code for a character.In some embodiments, analog data may be represented by a near continuumof measurable values within a particular range.

Information, including instructions for reconstructing a trajectory fromanonymized data, may be provided to the bus 910 for use by the processor902 from an external input device 912, such as a keyboard or a sensor.The sensor may be configured to detect conditions in its vicinity andtransform those detections into physical expression compatible with themeasurable phenomenon used to represent information in computer system900. Other external devices coupled to bus 910, used primarily forinteracting with humans, may include a display device 914, such as acathode ray tube (CRT) or a liquid crystal display (LCD), or plasmascreen or printer for presenting text or images, as well as a pointingdevice 916 (e.g. a mouse, trackball, cursor direction keys, motionsensor, and so forth) for controlling a position of a small cursor imagepresented on the display 914 and issuing commands associated withgraphical elements presented on the display 914. In some embodiments,for example, the computer system 900 performs all functionsautomatically without human input. As such, one or more of externalinput device 912, display device 914 and pointing device 916 may beomitted.

As shown in FIG. 9, special purpose hardware, such as an applicationspecific integrated circuit (ASIC) 920, may be coupled to bus 910. Thespecial purpose hardware may be configured to perform specializedoperations. Examples of ASICs include graphics accelerator cards forgenerating images for display 914, cryptographic boards for encryptingand decrypting messages sent over a network, speech recognition, andinterfaces to special external devices, such as robotic arms and medicalscanning equipment that repeatedly perform some complex sequence ofoperations that are more efficiently implemented in hardware.

The computer system 900 may also include one or more instances of acommunications interface 970 coupled to bus 910. The communicationinterface 970 may provide a one-way or two-way communication coupling toa variety of external devices that operate with their own processors,such as printers, scanners and external disks. In addition, thecommunication interface 970 may provide a coupling to a local network980, by way of a network link 978. The local network 980 may provideaccess to a variety of external devices and systems, each having theirown processors and other hardware. For example, as shown in FIG. 9, thenetwork link 978 can communicate with a local network 980, which may bein communication with a host 982 and/or internet service provider (ISP)984. In turn, the ISP 984 may communicate with a remote server 988 viathe internet 986.

By way of example, the communication interface 970 may include aparallel port or a serial port or a universal serial bus (USB) port on apersonal computer. In some embodiments, the communications interface 970may include one or more integrated services digital network (ISDN)cards, or digital subscriber line (DSL) cards, or telephone modems thatprovides an information communication connection to a corresponding typeof telephone line. In some embodiments, the communication interface 970may include a cable modem that converts signals on bus 910 into signalsfor a communication connection over a coaxial cable or into opticalsignals for a communication connection over a fiber optic cable. Asanother example, the communications interface 970 may be a local areanetwork (LAN) card configured to provide a data communication connectionto a compatible LAN, such as Ethernet. Wireless links may also beimplemented. For wireless links, the communications interface 970 maysend and/or receive electrical, acoustic or electromagnetic signals,including infrared and optical signals, that carry information streams,including digital data. For example, in wireless handheld devices (e.g.mobile telephones, cell phones, and so forth), the communicationsinterface 970 may include a radio band electromagnetic transmitter andreceiver called a radio transceiver. In certain embodiments, thecommunications interface 970 enables connection to the communicationnetwork, as described with reference to FIG. 2.

As used herein, computer-readable media refers to any media thatparticipates in providing information to processor 902, includinginstructions for execution. Such media may take many forms, and includenon-volatile media, volatile media, transmission media, and others.Non-volatile media include, for example, optical or magnetic disks, suchas storage device 908. Volatile media include, for example, dynamicmemory 904. Transmission media include, for example, coaxial cables,copper wire, fiber optic cables, and carrier waves that travel throughspace without wires or cables, such as acoustic waves andelectromagnetic waves, including radio, optical and infrared waves.Signals include man-made transient variations in amplitude, frequency,phase, polarization or other physical properties transmitted through thetransmission media. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium,punch cards, paper tape, optical mark sheets, any other physical mediumwith patterns of holes or other optically recognizable indicia, a RAM, aPROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, acarrier wave, or any other medium from which a computer can read.

Turning now to FIG. 10, a chip set 1000, in accordance with aspects ofthe present disclosure, is illustrated. In some implementations, thechip set 1000 may be programmed to carry out steps in accordance withmethods described herein, and may include various components (e.g. asdescribed with respect to FIG. 9) incorporated in one or more physicalpackages (e.g., chips). By way of example, a physical package includesan arrangement of one or more materials, components, and/or wires on astructural assembly (e.g., a baseboard) that provides one or morecharacteristics, such as physical strength, conservation of size, and/orlimitation of electrical interaction. It is contemplated that in certainembodiments the chip set 1000 can be implemented in a single chip.

As shown, the chip set 1000 may include a communication mechanism, suchas a bus 1001 for passing information and data among the components ofthe chip set 1000. A processor 1003 connected to the bus 1001 may beconfigured to execute instructions and process information stored in,for example, a memory 1005. The processor 1003 may include one or moreprocessing cores, with each core capable of performing independently. Insome implementations, a multi-core processor may be used, which enablesmultiprocessing within a single physical package. Examples of amulti-core processor include two, four, eight, or greater numbers ofprocessing cores. Alternatively, or additionally, the processor 1003 mayinclude one or more microprocessors configured in tandem, via the bus1001, to perform independent execution of instructions, pipelining, andmultithreading.

The chip set 1000 may also include specialized components configured toperform certain processing functions and tasks. For instance, the chipset 1000 may include one or more digital signal processors (DSP) 1007,or one or more application-specific integrated circuits (ASIC) 1009, orboth. In particular, the DSP 1007 may be configured to processreal-world signals (e.g., sound) in real time independently of theprocessor 1003. Similarly, the ASIC 1009 may be configured to performedspecialized functions not easily performed by a general-purposeprocessor. Other specialized components to aid in performing theinventive functions described herein may include one or more fieldprogrammable gate arrays (FPGA) (not shown), one or more controllers(not shown), or one or more other special-purpose computer chips.

The processor 1003 and accompanying components may have connectivity tothe memory 1005 via the bus 1001, as shown. The memory 1005 may includedynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.),static memory (e.g., ROM, CD-ROM, etc.), and others, configured forstoring executable instructions. The instructions, when executed,perform a variety of steps, including steps for identifying the qualityof terrestrial data, in accordance with methods described herein. Thememory 1005 may also store the data associated with or generated by theexecution.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder. It should be appreciated that many equivalents, alternatives,variations, and modifications, aside from those expressly stated, arepossible and may be considered within the scope of the invention.

1. A method for reconstructing a trajectory from anonymized data, themethod comprising: receiving anonymized data corresponding to atrajectory traversed by a user or object; assembling a state-space modelhaving internal states that represent geographical coordinates of thetrajectory; for a selected trajectory segment associated with theanonymized data, predicting future locations of the user or object usingthe state-space model and particle filtering; reconstructing thetrajectory using the future locations; and generating a reportindicative of the trajectory.
 2. The method of claim 1, wherein themethod further comprises generating the anonymized data by dividing thetrajectory into trajectory segments separated by one or more predefinedgaps.
 3. The method of claim 1, wherein the method further comprisesrandomly initializing a set of particles corresponding to the selectedtrajectory segment.
 4. The method of claim 3, wherein the method furthercomprises assigning initial weights to the set of particles.
 5. Themethod of claim 3, wherein the method further comprises applying atransition to the set of particles, wherein the transition isrepresented by a linear model or a non-linear model.
 6. The method ofclaim 4, wherein the method further comprises using the anonymized datato generate probability scores, wherein each probability scorecorresponding to a likelihood of transition between different trajectorysegments. The method of claim 5, wherein the method further comprises:a. updating the weights of the set of particles; b. resampling the setof particles based on the updated weights to generate a set of resampledparticles; c. predicting a future location at a future time point usingthe set of resampled particles; d. repeating steps a)-d) to predict thefuture locations of the user or object.
 8. The method of claim 7,wherein the method further comprises applying a Sequential ImportanceResampling (SIR) in predicting the future locations of the user orobject.
 9. The method of claim 1, wherein the method further comprisesreconstructing the trajectory by linking together at least twotrajectory segments based on scores and durations of the segmenttrajectories.
 10. The method of claim 2, wherein the method furthercomprises characterizing, based on the trajectory, an anonymizationtechnique used to generate the anonymized data.
 11. A system forreconstructing a trajectory from anonymized data, the system comprising:at least one processor; at least one memory comprising instructionsexecutable by the at least one processor, the instructions causing thesystem to: receive anonymized data corresponding to a trajectorytraversed by a user or object; assemble a state-space model havinginternal states that represent geographical coordinates of thetrajectory; for a selected trajectory segment associated with theanonymized data, predict future locations of the user or object usingthe state-space model and particle filtering; reconstruct the trajectoryusing the future locations; and generate a report indicative of thetrajectory; and a display for providing the report.
 12. The system ofclaim 11, wherein the instructions further cause the system to generatethe anonymized data by dividing the trajectory into trajectory segmentsseparated by one or more predefined gaps.
 13. The system of claim 11,wherein the instructions further cause the system to randomly initializea set of particles corresponding to the selected trajectory segment. 14.The system of claim 13, wherein the instructions further cause thesystem to assign initial weights to the set of particles.
 15. The systemof claim 13, wherein the instructions further cause the system to applya transition to the set of particles, wherein the transition isrepresented by a linear model or a non-linear model.
 16. The system ofclaim 14, wherein the instructions further cause the system to use theanonymized data to generate probability scores, wherein each probabilityscore corresponding to a likelihood of transition between differenttrajectory segments.
 17. The system of claim 15, wherein theinstructions further cause the system to: a. updating the weights of theset of particles; b. resampling the set of particles based on theupdated weights to generate a set of resampled particles; c. predictinga future location at a future time point using the set of resampledparticles; d. repeating steps a)-d) to predict the future locations ofthe user or object.
 18. The system of claim 17, wherein the instructionsfurther cause the system to apply a Sequential Importance Resampling(SIR) in predicting the future locations of the user or object.
 19. Thesystem of claim 11, wherein the instructions further cause the system toreconstruct the trajectory by linking together at least two trajectorysegments based on scores and durations of the segment trajectories. 20.A non-transitory computer-readable storage medium for reconstructing atrajectory from anonymized data, carrying one or more sequences of oneor more instructions which, when executed by one or more processors,cause an apparatus to perform steps to: receive anonymized datacorresponding to a trajectory traversed by a user or object; assemble astate-space model having internal states that represent geographicalcoordinates of the trajectory; for a selected trajectory segmentassociated with the anonymized data, predict future locations of theuser or object using the state-space model and particle filtering;reconstruct the trajectory using the future locations; and generate areport indicative of the trajectory.